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Road Accident Severity Prediction in Mauritius using Supervised Machine Learning Algorithms

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dc.contributor.author Sowdagur, Jameel Ahmad
dc.contributor.author Rozbully-Sowdagur, B. Tawheeda B
dc.contributor.author Suddul, Geerish
dc.date.accessioned 2022-02-25T04:24:04Z
dc.date.available 2022-02-25T04:24:04Z
dc.date.issued 2021
dc.identifier.citation Sowdagur Jameel Ahmad, Rozbully-Sowdagur B. Tawheeda B, Suddul Geerish (2021), Road Accident Severity Prediction in Mauritius using Supervised Machine Learning Algorithms, International Conference on Advances in Computing and Technology (ICACT–2021) Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka 153-157 en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/24512
dc.description.abstract Road accidents with high severities are a major concern worldwide, imposing serious problems to the socio-economic development. Several techniques exist to analyse road traffic accidents to improve road safety performance. Machine learning and data mining which are novel approaches are proposed in this study to predict accident severity. Support Vector Machine (SVM), Gradient Boosting (GB), Logistic Regression (LR), Random Forest (RF) and Naïve Bayes (NB) were applied to perform effective data analysis for informed decisions using Python programming language. The gradient boosting outperformed all the other models in predicting the severity outcomes, yielding an overall accuracy of 83.2% and an AUC of 83.9% en_US
dc.publisher Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka en_US
dc.subject Data mining, machine learning, accident severity. en_US
dc.subject . en_US
dc.title Road Accident Severity Prediction in Mauritius using Supervised Machine Learning Algorithms en_US


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